AB0058 SERUM ANTIGENOME PROFILING REVEALS DIAGNOSTIC MODELS FOR RHEUMATOID ARTHRITIS
Authors: P. Han, C. Hou, X. Zheng, L. Cao, X. Shi, X. Zhang, H. Ye, T. Li, F. Hu, Z. Li
Journal: Annals of the Rheumatic Diseases (2022)

<h3>Background</h3> Rheumatoid arthritis (RA) is a chronic autoimmune disease that leads to joint damage, systemic inflammation and early mortality. Though the precise molecular mechanism in the triggering immune response are not fully understood, the emergence of antibodies against self-antigens can serve as diagnostic biomarker. Multiple antigens have been confirmed. However, the profiling of serum antigen, antigenome, remains poorly known. <h3>Objectives</h3> The study aimed to investigate the serum antigenomic profiling and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm. <h3>Methods</h3> We captured serum antigens from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), sex- and age-matched 30 osteoarthritis patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed. We then trained a machine learning model to classify RA, ACPA-positive RA and ACPA-negative RA based on proteomic data and validated in the cohort. <h3>Results</h3> We identified 62, 71 and 49 differentially expressed proteins (DEPs) in RA, ACPA-positive RA and ACPA-negative RA respectively, compared to OA and healthy controls. Among these DEPs, the pathway enrichment analysis and protein-protein interactions networks were conducted. Three panels were constructed to classify RA, ACPA-positive RA and ACPA-negative RA using random forest models algorithm based on the molecular signature of DEPs, whose area under curve (AUC) were calculated as 0.9949 (95% CI = 0.9792-1), 0.9913 (95%CI = 0.9653-1) and 1.0 (95% CI = 1-1). <h3>Conclusion</h3> This study presented serum antigen profiling of RA. Among them, three panels of antigens were identified to classify RA, ACPA-positive and ACPA-negative RA patients as diagnostic biomarkers. <h3>References</h3> [1]Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. <i>Lancet (London, England</i>). (2016) 388: 2023-38. doi: 10.1016/S0140-6736(16)30173-8 [2]De Rycke L, Peene I, Hoffman IE, Kruithof E, Union A, Meheus L, et al. Rheumatoid factor and anticitrullinated protein antibodies in rheumatoid arthritis: diagnostic value, associations with radiological progression rate, and extra-articular manifestations. <i>Ann Rheum Dis</i>. (2004) 63: 1587-93. doi: 10.1136/ard.2003.017574 [3]Kampstra ASB, Dekkers JS, Volkov M, Dorjée AL, Hafkenscheid L, Kempers AC, et al. Different classes of anti-modified protein antibodies are induced on exposure to antigens expressing only one type of modification. <i>Ann Rheum Dis</i>. (2019) 78: 908-16. doi: 10.1136/annrheumdis-2018-214950 [4]Liao W, Li Z, Li T, Zhang Q, Zhang H, Wang X. Proteomic analysis of synovial fluid in osteoarthritis using swath‑mass spectrometry. <i>Mol Med Rep</i>. (2018) 17: 2827-36. doi: 10.3892/mmr.2017.8250 [5]Peffers MJ, Smagul A, Anderson JR. Proteomic analysis of synovial fluid: current and potential uses to improve clinical outcomes. <i>Expert Rev Proteomic</i>. (2019) 16: 287-302. doi:10.1080/14789450.2019.1578214 [6]Swan AL, Mobasheri A, Allaway D, Liddell S, Bacardit J. Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. <i>Omics: a journal of integrative biology</i>. (2013) 17: 595-610. doi: 10.1089/omi.2013.0017 [7]Mahler M, Martinez-Prat L, Sparks JA, Deane KD. Precision medicine in the care of rheumatoid arthritis: focus on prediction and prevention of future clinically-apparent disease. <i>Autoimmun Rev</i>. (2020) 19: 102506. doi: 10.1016/j.autrev.2020.102506 [8]Mun S, Lee J, Park A, Kim HJ, Lee YJ, Son H, et al. Proteomics approach for the discovery of rheumatoid arthritis biomarkers using mass spectrometry. <i>Int J Mol Sci</i>. (2019) 20. doi: 10.3390/ijms20184368 [9]Li K, Mo W, Wu L, Wu X, Luo C, Xiao X, et al. Novel autoantibodies identified in acpa-negative rheumatoid arthritis. <i>Ann Rheum Dis</i>. (2021). doi: 10.1136/annrheumdis-2020-218460 <h3>Disclosure of Interests</h3> None declared